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Google Professional Machine Learning Engineer Exam - Topic 9 Question 3 Discussion

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?
D) Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
A) Use Al Platform for distributed training
B) Create a cluster on Dataproc for training
C) Create a Managed Instance Group with autoscaling

Google Professional Machine Learning Engineer Exam - Topic 9 Question 3 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 3
Topic #: 9
[All Professional Machine Learning Engineer Questions]

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

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Suggested Answer: D

by Golda at May 05, 2022, 09:45 PM

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Oretha
10 months ago
Not sure if A is the easiest way to minimize refactoring though.
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Teddy
10 months ago
Totally agree with A, it's designed for this!
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Lamonica
11 months ago
Wait, isn't D overkill for just training models?
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Francesco
11 months ago
I think B could work too, but it might be more complex.
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Janna
11 months ago
A is the best option for distributed training!
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Wilda
11 months ago
I feel like Managed Instance Groups are more about scaling applications rather than training models. I’m leaning towards AI Platform for this scenario.
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Nieves
11 months ago
I practiced a similar question where we had to choose between different Google Cloud services. I think Kubeflow Pipelines could be useful, but it might require more setup than AI Platform.
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Laura
11 months ago
I'm not entirely sure, but I think using Dataproc could be overkill for just training models. We might need something simpler to minimize refactoring.
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Alyce
11 months ago
I remember we discussed using AI Platform for distributed training in class. It seems like a good fit since it supports TensorFlow and can handle large datasets.
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Dorothy
11 months ago
Okay, I think I've got this. The "Equipment" category is focused on issues with the actual tools, machines, and measurement systems used in the process. So all of these - out of calibration measurement, tolerance changes, and worn bearings - would be the kinds of equipment-related problems that could lead to defects. I'm feeling good about this one.
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Roosevelt
11 months ago
Hmm, I'm a bit confused on this one. I know the default route is important, but I'm not sure about the specifics. I'll have to think this through carefully.
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Chana
11 months ago
I'm not entirely sure, but I think 850 nm might still have some glow based on that practice question we did on visible vs. infrared wavelengths.
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